Acceptability of the Fitbit in behavioural activation therapy for depression: a qualitative study
Bibliographic record
Abstract
INTRODUCTION: Major depressive disorder is characterised by low mood and poor motivation. Literature suggests that increased physical activity has positive effects on alleviating depression. Fitness-tracking devices may complement behavioural activation (BA) therapy to improve physical activity and mental health in patients with depression. OBJECTIVES: To understand patients' perceived benefit from the Fitbit and explore themes associated with patient experiences. To compare perceived benefit, patient factors, Fitbit usage and Beck's Depression Inventory (BDI) scores. METHODS: Semistructured interviews were conducted with patients (n=36) who completed a 28-week BA group programme in a mood disorders outpatient clinic. All patients were asked to carry a Fitbit One device. We conducted thematic analyses on the interviews and exploratory quantitative analyses on patient characteristics, Fitbit usage, steps recorded, perceived benefit and BDI scores. FINDINGS: Twenty-three patients found the Fitbit helpful for their physical activity. Themes of positive experiences included self-awareness, peer motivation and goal-setting opportunities. Negative themes included inconvenience, inaccuracies and disinterest. Age, baseline and change in BDI scores, prior physical activity goals and familiarity with technology were not associated with perceived benefit from the Fitbit or usage. Perceived benefit was significantly (p<0.01) associated with usage. CONCLUSIONS: Overall, the Fitbit is an acceptable tool to complement BA therapy for patients with depression. Many positive themes were concordant with current literature; however, patients also reported negative aspects that may affect use. CLINICAL IMPLICATIONS: Clinicians and researchers should consider both strengths and limitations of activity trackers when implementing them to motivate patients with depression. TRIAL REGISTRATION NUMBER: NCT02045771; Pre-results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".